Date: Wednesday 26th January 2022
Time: 14:00-15:00 GMT
Speakers: Kevin Ding (AstraZeneca) and Juan Abellan (GSK)
Who is this event intended for? Statisticians working on regulatory submissions
What is the benefit of attending? To learn more about the 'how' and 'why' of tipping point analysis, using relevant examples from previous studies.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = £20+VAT
To register for the session, please click here.
To assess the strength of clinical study findings, regulatory authorities often request tipping point analyses. However what is a tipping point analysis, and how is one performed? What are the different approaches for continuous, binary and time to event data? Kevin and Juan present some practical examples.
To view the flyer for this event, please click here.
|
Speaker |
Biography |
Abstract |
|
|
Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.
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Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed. |
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Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.
|
Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective. |
Date: Wednesday 26th January 2022
Time: 14:00-15:00 GMT
Speakers: Kevin Ding (AstraZeneca) and Juan Abellan (GSK)
Who is this event intended for? Statisticians working on regulatory submissions
What is the benefit of attending? To learn more about the 'how' and 'why' of tipping point analysis, using relevant examples from previous studies.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = £20+VAT
To register for the session, please click here.
To assess the strength of clinical study findings, regulatory authorities often request tipping point analyses. However what is a tipping point analysis, and how is one performed? What are the different approaches for continuous, binary and time to event data? Kevin and Juan present some practical examples.
To view the flyer for this event, please click here.
|
Speaker |
Biography |
Abstract |
|
|
Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.
|
Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed. |
|
|
Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.
|
Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective. |
Date: Wednesday 26th January 2022
Time: 14:00-15:00 GMT
Speakers: Kevin Ding (AstraZeneca) and Juan Abellan (GSK)
Who is this event intended for? Statisticians working on regulatory submissions
What is the benefit of attending? To learn more about the 'how' and 'why' of tipping point analysis, using relevant examples from previous studies.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = £20+VAT
To register for the session, please click here.
To assess the strength of clinical study findings, regulatory authorities often request tipping point analyses. However what is a tipping point analysis, and how is one performed? What are the different approaches for continuous, binary and time to event data? Kevin and Juan present some practical examples.
To view the flyer for this event, please click here.
|
Speaker |
Biography |
Abstract |
|
|
Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.
|
Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed. |
|
|
Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.
|
Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective. |
Date: Wednesday 26th January 2022
Time: 14:00-15:00 GMT
Speakers: Kevin Ding (AstraZeneca) and Juan Abellan (GSK)
Who is this event intended for? Statisticians working on regulatory submissions
What is the benefit of attending? To learn more about the 'how' and 'why' of tipping point analysis, using relevant examples from previous studies.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = £20+VAT
To register for the session, please click here.
To assess the strength of clinical study findings, regulatory authorities often request tipping point analyses. However what is a tipping point analysis, and how is one performed? What are the different approaches for continuous, binary and time to event data? Kevin and Juan present some practical examples.
To view the flyer for this event, please click here.
|
Speaker |
Biography |
Abstract |
|
|
Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.
|
Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed. |
|
|
Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.
|
Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective. |
Date: Wednesday 26th January 2022
Time: 14:00-15:00 GMT
Speakers: Kevin Ding (AstraZeneca) and Juan Abellan (GSK)
Who is this event intended for? Statisticians working on regulatory submissions
What is the benefit of attending? To learn more about the 'how' and 'why' of tipping point analysis, using relevant examples from previous studies.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = £20+VAT
To register for the session, please click here.
To assess the strength of clinical study findings, regulatory authorities often request tipping point analyses. However what is a tipping point analysis, and how is one performed? What are the different approaches for continuous, binary and time to event data? Kevin and Juan present some practical examples.
To view the flyer for this event, please click here.
|
Speaker |
Biography |
Abstract |
|
|
Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.
|
Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed. |
|
|
Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.
|
Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective. |
Date: Wednesday 26th January 2022
Time: 14:00-15:00 GMT
Speakers: Kevin Ding (AstraZeneca) and Juan Abellan (GSK)
Who is this event intended for? Statisticians working on regulatory submissions
What is the benefit of attending? To learn more about the 'how' and 'why' of tipping point analysis, using relevant examples from previous studies.
You can now register for this event. Registration fees are as follows:
- Members of PSI = Free of charge
- Non-Members of PSI = £20+VAT
To register for the session, please click here.
To assess the strength of clinical study findings, regulatory authorities often request tipping point analyses. However what is a tipping point analysis, and how is one performed? What are the different approaches for continuous, binary and time to event data? Kevin and Juan present some practical examples.
To view the flyer for this event, please click here.
|
Speaker |
Biography |
Abstract |
|
|
Juan is a statistician by training, by experience and by passion. He studied Maths and Stats and did his PhD at the University of Valencia (Spain), and worked as a research fellow at Imperial College London. He has worked as a statistician in various roles in Public Offices, Academia and Industry in Spain, Germany and the UK. He's currently a GSK fellow and Statistics Director at the Statistics and Data Science Innovation Hub, based in London. His current main interests include the use of Bayesian thinking for better quantitative decision making in drug development, estimands and their estimation in the presence of missing data and the analysis of data collected with digital wearable technologies.
|
Tipping point sensitivity analysis for time-to-event: a case study in belimumab In this presentation I will illustrate one way of implementing a tipping point analysis (TPA) for a time-to-event endpoint to assess the robustness of results to the censoring-at-random assumption. The method is based on multiple imputation and it assumes the hazard rate post-censoring changes. A grid of values reflecting such changes is considered to vary the hazard rate post-censoring independently for each treatment arm. The (experimental, control) pairs of post-censoring hazard rate changes form the TPA scenarios. Within each of the TPA scenarios, participants who are censored are imputed a time to the event of interest and are administratively censored if the imputed time exceeds the length of the follow-up. Results are combined across imputed datasets using Rubin’s rules. Finally, the plausibility of the scenarios where the results tip is discussed. |
|
|
Kevin has 13 years of industry experience of designing and implementing study design of clinical trials in the areas of both general medicine and oncology. Kevin previously worked in J&J (late phase oncology) and Novartis (late phase immunology) and now is working in AZ as Statistical Science Associate Director for early phase oncology. Kevin has 14 academic journal publications and has special research interest in practical use of estimand and approaches of dealing with missing data in late phase trials. He presented “The Application of Tipping Point Analysis in Clinical Trials” in 2018 JSM meeting.
|
Practical use of Tipping Point Analysis in regulatory submissions of clinical trials The tipping point analysis (TPA) approach has gained popularity recently as an approach for performing the sensitivity analysis under the missing not at random (MNAR) assumption. This presentation will review why TPA gets popular in clinical trial submissions, its implementation for binary endpoints for time-independent imputation and time-dependent imputation, general procedure of TPA implementation for continuous endpoints, and Interpretation of TPA result based on clinical input. The presentation then shows six real examples of FDA statistical review of submitted NDA/BLAs (all in public domain) that use TPA as sensitivity analysis to illustrate the practical use of this method in real trials and important consideration points from the regulatory perspective. |
An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.
Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.
The book club’s usual focus is to read and discuss professional development books. In this short format event you can more easily develop you career without the commitment of reading the whole book - simply listen to the 1-hour long podcast before joining the interactive session on 21 May.
This webinar is organised by the RWD SIG and the Historical Data SIG. We will review recent methods, applications, and tools of integrating subject-level-data from clinical trial with external data using Bayesian methods and/or causal inference methods.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This will be a 45 minute webinar which will explain the topic presented in the published paper, ‘Applying the Estimand Framework to Clinical Pharmacology Trials with a Case Study in Bioequivalance’. There will be 15 minutes for a panel Q&A with some of the authors following the presentation.
This 1-hour webinar will be an opportunity to hear about the methodology and first results of the iRISE consortium. iRISE is working towards a better understanding of reproducibility and the interventions that work to improve it. At the end of the presentation there will also be the opportunity to ask questions.
This one-day event focuses on the comprehensive management of transitioning to R/Open-Source, addressing the challenges and providing actionable insights. Attendees will participate in sessions covering essential topics such as training best practices, creating strategic plans, making the case to senior management, and managing both statistical and programming aspects of the transition.
Develop your non-technical skills by reading The Art of Explanation by Ros Atkins and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply skills from the book in-between sessions.
This course is aimed at biostatisticians with no or some pediatric drug development experience who are interested to further their understanding. We will give you an introduction to the pediatric drug development landscape. This will include identifying the key regulations and processes governing pediatric development, a discussion on the needs and challenges when conducting pediatric research and a focus on the ways to overcome these challenges from a statistical perspective.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
The program will feature insightful sessions led by distinguished invited speakers, alongside a poster session showcasing the latest advancements in the field. Further details will be provided.
This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.
This is an exciting, new opportunity for an experienced Statistician looking to take the next step in their career. Offered as a remote or hybrid position aligned with our site in Harrogate, North Yorkshire.
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